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Artificial intelligence foundation for therapeutic science

Nature Chemical Biology volume 18, pages 1033–1036 (2022)Cite this article

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Artificial intelligence (AI) is poised to transform therapeutic science. Therapeutics Data Commons is an initiative to access and evaluate AI capability across therapeutic modalities and stages of discovery, establishing a foundation for understanding which AI methods are most suitable and why.

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Fig. 1: Overview of Therapeutics Data Commons.
Fig. 2: AI-ready datasets, machine learning tasks and benchmarks in Therapeutics Data Commons.
Fig. 3: Example use cases of Therapeutics Data Commons.

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Acknowledgements

K.H. and M.Z. gratefully acknowledge the support of US National Science Foundation (NSF) awards IIS-2030459 and IIS-2033384, US Air Force contract no. FA8702-15-D-0001 and awards from the Harvard Data Science Initiative, Amazon Research, Bayer Early Excellence in Science, AstraZeneca Research and the Roche Alliance with Distinguished Scientists. W.G. was supported by the US Office of Naval Research under grant no. N00014-21-1-2195. C.W.C. was supported by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) Consortium. J.S. was supported by NSF awards SCH-2014438, IIS-1838042, US National Institutes of Health (NIH) award 1R01NS107291-01, and OSF Healthcare. J.L. was supported by the US Defense Advanced Research Progress Agency under awards HR00112190039, N660011924033; the Army Research Organization under nos. W911NF-16-1-0342, W911NF-16-1-0171; the NSF under nos. OAC-1835598, OAC-1934578, CCF-1918940; the NIH under no. 3U54HG010426-04S1; and the Stanford Data Science Initiative, the Wu Tsai Neurosciences Institute, Amazon, Docomo, GSK, Hitachi, Intel, JPMorgan Chase, Juniper Networks, KDDI, NEC and Toshiba. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders.

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Author notes

  1. Kexin Huang

    Present address: Department of Computer Science, Stanford University, Stanford, CA, USA

  2. These authors contributed equally: Kexin Huang, Tianfan Fu, Wenhao Gao

Authors and Affiliations

  1. Health Data Science Program, Harvard University, Boston, MA, USA

    Kexin Huang

  2. College of Computing, Georgia Institute of Technology, Atlanta, GA, USA

    Tianfan Fu

  3. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Wenhao Gao & Connor W. Coley

  4. Heinz College, Carnegie Mellon University, Pittsburgh, PA, USA

    Yue Zhao

  5. School of Medicine, Stanford University, Stanford, CA, USA

    Yusuf Roohani

  6. Department of Computer Science, Stanford University, Stanford, CA, USA

    Jure Leskovec

  7. Analytics Center of Excellence, IQVIA, Cambridge, MA, USA

    Cao Xiao

  8. Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA

    Jimeng Sun

  9. Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, MA, USA

    Marinka Zitnik

  10. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Marinka Zitnik

  11. Harvard Data Science Initiative, Cambridge, MA, USA

    Marinka Zitnik

Authors
  1. Kexin Huang
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Contributions

K.H., T.F., W.G. and M.Z. designed the data management and computational infrastructure. K.H., T.F., W.H., Y.Z., Y.R. and M.Z. implemented the programming interface and software package. K.H., T.F., W.H. and Y.R. retrieved, processed and harmonized datasets. K.H. and M.Z. designed and implemented the website. K.H., T.F., W.H., Y.Z., Y.R., J.L., C.C, C.X., J.S. and M.Z. wrote and edited the manuscript. M.Z. conceived and supervised the study.

Corresponding author

Correspondence to Marinka Zitnik.

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The authors declare no competing interests.

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Huang, K., Fu, T., Gao, W. et al. Artificial intelligence foundation for therapeutic science. Nat Chem Biol 18, 1033–1036 (2022). https://doi.org/10.1038/s41589-022-01131-2

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